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67 results for “Regression analysis”
Multi-institutional Analysis of Survival and Recurrence Patterns of Different Pathological Regression Types After Neoadjuvant Chemoradiotherapy or Radiotherapy for Esophageal Squamous Cell Carcinoma
ClinicalTrials.gov study NCT06241079. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Genome-wide analysis of tumor regression upon K-RAS inactivation
GEO Series GSE40606. Mus musculus. 12 samples. Type: Expression profiling by array.
Multiple regression analysis of a comprehensive transcriptomic data assembly elucidates mechanically- and biochemically-driven responses to focused ultrasound blood-brain barrier disruption - addition
GEO Series GSE184751. Mus musculus. 75 samples. Type: Expression profiling by high throughput sequencing.
Single-cell landscape analysis reveals distinct regression trajectories and novel prognostic biomarkers in primary neuroblastoma
GEO Series GSE287352. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
Data set and results of regression analysis of gene variants in Prostate Cancer patients from Mexico-mestizo population.
<p>Genotyping assays were performed using TaqMan SNP Genotyping Assays probes C_27532228_20 and C_2362601_10 (Life Technologies, Carlsbad, CA, USA).</p> <p>Thermalcycler: StepOnePlus™ Real-Time PCR (Thermo Fisher Scientific, Waltham, MA, USA).</p> <p>Sample: genomic DNA from blood.</p> <p>Subjects: Prostate cancer patients and controls (healthy and benign prostatic hyperplasia). </p> <p>Results of Regression Anlysis for clinical features of gene SRD5A2 variants (rs9282858 and<strong> </strong>rs523349) in prostate cancer patients from Mexican-mestizo population.</p> <p>Starting with the full model, successive models are created, each one using one less regressor (or covariate) than the previous model. Each of the regressors currently in the model is removed to create a "trial" model excluding that regressor. The p-value of the current model (or full model) versus the trial model (or reduced model) is calculated, and the model with the smallest p-value is used as the next model. This method removes the least significant variable from the current model. If every p-value is smaller than the p-value cut off specified, the backward elimination method stops. The method also stops if all variables have been removed from the model, or if all variables left are included in the original reduced model. From the standpoint of further analysis, the final model becomes the "full model" for this set of potential regressors</p> <p>Ethics approval and consent to participate in the study: The protocol and informed consent were approved by the Ethics and Research Committee of the School of Medicine (Universidad Autonoma de Nuevo Leon), with registration number UR16-00007. In the data set, no data was included that compromises the confidentiality of the participating subjetcs.</p> <pre> </pre>
Use of Vegetation Change Tracker, Spatial Analysis, and Random Forest Regression to Assess the Evolution of Plantation Stand Age in Southeast China
<p>It is crucial to determine the spatio-temporal distribution patterns of forest ages across wide regions, as forest management plans and practices, and ecosystem carbon budgeting are highly dependent on these. However, given frequent deforestation events (e.g., harvesting) and rapid recovery of plantation stands in Southern China, field-based forest age measurements over wide regions are time-consuming, labour-intensive, and costly. In the current study, we mapped the spatio-temporal patterns of forest stand ages across three typical plantations in Southern China. This was accomplished by using two new feasible and accurate methods, 1) integrating vegetation change tracker (VCT) algorithm and spatial analysis (VCT-SA) for the pixels that were disturbed at least once from 1987 to 2017, and 2) integrating VCT and random forest (VCT-RF) for the pixels were not disturbed during the study period. The results revealed the spatio-temporal distribution of age structure, which indicated that the plantation stands in our large study area were increasingly aging.</p>
Dataset related to article: "Different Oncologic Outcomes in Early-Onset and Late-Onset Sporadic Colorectal Cancer: A Regression Analysis on 2073 Patients"
<p>This record contains data related to article: "Different Oncologic Outcomes in Early-Onset and Late-Onset Sporadic Colorectal Cancer: A Regression Analysis on 2073 Patients"</p> <p>The incidence of colorectal cancer (CRC) is increasing in the population aged ≤ 49 (early-onset CRC-EOCRC). Recent studies highlighted the biological and clinical differences between EOCRC and late-onset CRC (LOCRC-age ≥ 50), while comparative results about long-term survival are still debated. This study aimed to investigate whether age of onset may impact on oncologic outcomes in a surgical population of sporadic CRC patients. Patients operated on for sporadic CRC from January 2010 to January 2022 were allocated to the EOCRC and LOCRC groups. The primary endpoint was the recurrence/progression-free survival (R/PFS). A total of 423 EOCRC and 1650 LOCRC was included. EOCRC had a worse R/PFS (<em>p</em> &lt; 0.0001) and cancer specific survival (<em>p</em> &lt; 0.0001) compared with LOCRC. At Cox regression analysis, age of onset, tumoral stage, signet ring cells, extramural/lymphovascular/perineural veins invasion, and neoadjuvant therapy were independent risk factors for R/P. The analysis by tumoral stage showed an increased incidence of recurrence in stage I EOCRC (<em>p</em> = 0.014), and early age of onset was an independent predictor for recurrence (<em>p</em> = 0.035). Early age of onset was an independent predictor for worse prognosis, this effect was stronger in stage I patients suggesting a potentially-and still unknown-more aggressive tumoral phenotype in EOCRC.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.